关键词: 渐进对抗学习, 弱监督定位, 对抗擦除

Abstract:

Aiming to solve the problem of lacking fine location annotations in many datasets in practical applications, a weakly supervised object localization algorithm based progressive adversarial learning is proposed. Specifically, in order to solve the problem of training difficulties caused by dataset noise, self-paced learning is introduced to sort the training data according to the principle of simplicity to difficulty. In the network design, the weakly supervised object localization network is designed as a multi-label classification network, and the corresponding adversarial loss function is proposed to adapt to the task. Finally, in order to solve the problem that the existing methods often only pay attention to the most distinguishing part and can not locate the whole target, a pyramid adversarial erase mechanism is proposed to find the complete target in the final location map. The experimental results on several benchmarks show that the proposed algorithm can achieve object localization on datasets with high accuracy. It has certain competitiveness with the most advanced method.

Key words: progressive adversarial learning, weakly supervised localization, adversarial erase